Overview

Dataset statistics

Number of variables17
Number of observations11430
Missing cells0
Missing cells (%)0.0%
Duplicate rows47
Duplicate rows (%)0.4%
Total size in memory1.5 MiB
Average record size in memory136.0 B

Variable types

Categorical8
Numeric9

Alerts

special has constant value ""Constant
Dataset has 47 (0.4%) duplicate rowsDuplicates
digits is highly overall correlated with entropy and 2 other fieldsHigh correlation
entropy is highly overall correlated with digits and 2 other fieldsHigh correlation
entropyRelative is highly overall correlated with ipHigh correlation
firstDigitIndex is highly overall correlated with digits and 2 other fieldsHigh correlation
ip is highly overall correlated with entropyRelativeHigh correlation
length is highly overall correlated with digits and 2 other fieldsHigh correlation
ip is highly imbalanced (92.6%)Imbalance
port is highly imbalanced (97.4%)Imbalance
doubleHyphen is highly imbalanced (97.4%)Imbalance
abnormal is highly imbalanced (56.5%)Imbalance
status is uniformly distributedUniform
firstDigitIndex has 6566 (57.4%) zerosZeros
digits has 6566 (57.4%) zerosZeros

Reproduction

Analysis started2024-02-11 16:53:37.649171
Analysis finished2024-02-11 16:54:17.920454
Duration40.27 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

status
Categorical

UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
5715 
1
5715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Length

2024-02-11T16:54:18.082648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:18.318946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring characters

ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5715
50.0%
1 5715
50.0%

ngrams
Real number (ℝ)

Distinct11235
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963.01058
Minimum78.480636
Maximum1539.6037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:18.549348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78.480636
5-th percentile613.66753
Q1851.83276
median979.93038
Q31102.7613
95-th percentile1244.2958
Maximum1539.6037
Range1461.123
Interquartile range (IQR)250.92852

Descriptive statistics

Standard deviation194.40304
Coefficient of variation (CV)0.20187009
Kurtosis0.57947349
Mean963.01058
Median Absolute Deviation (MAD)125.27878
Skewness-0.6130154
Sum11007211
Variance37792.54
MonotonicityNot monotonic
2024-02-11T16:54:18.850598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
923.2873079 18
 
0.2%
485.1057218 11
 
0.1%
945.2990418 9
 
0.1%
964.0568935 8
 
0.1%
931.8600438 7
 
0.1%
593.0789854 7
 
0.1%
468.0068376 6
 
0.1%
874.5202359 6
 
0.1%
774.0251086 6
 
0.1%
902.2773835 6
 
0.1%
Other values (11225) 11346
99.3%
ValueCountFrequency (%)
78.48063572 1
 
< 0.1%
79.90797705 1
 
< 0.1%
156.6626466 1
 
< 0.1%
172.615394 1
 
< 0.1%
195.0868605 1
 
< 0.1%
199.8608947 4
< 0.1%
209.3858814 4
< 0.1%
216.1955867 1
 
< 0.1%
216.685719 1
 
< 0.1%
217.5066774 1
 
< 0.1%
ValueCountFrequency (%)
1539.603657 1
< 0.1%
1490.923178 1
< 0.1%
1482.335932 1
< 0.1%
1467.89459 1
< 0.1%
1458.438025 1
< 0.1%
1457.269843 1
< 0.1%
1444.483439 1
< 0.1%
1440.495382 1
< 0.1%
1439.838831 1
< 0.1%
1437.751096 1
< 0.1%

entropy
Real number (ℝ)

HIGH CORRELATION 

Distinct8493
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2333486
Minimum2.9528195
Maximum5.9969517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:19.164856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9528195
5-th percentile3.6339741
Q13.9675858
median4.1883311
Q34.4531556
95-th percentile4.9743113
Maximum5.9969517
Range3.0441322
Interquartile range (IQR)0.48556982

Descriptive statistics

Standard deviation0.41329349
Coefficient of variation (CV)0.097628031
Kurtosis1.2029799
Mean4.2333486
Median Absolute Deviation (MAD)0.23834113
Skewness0.73478956
Sum48387.175
Variance0.17081151
MonotonicityNot monotonic
2024-02-11T16:54:19.460573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.740601563 27
 
0.2%
3.844106545 27
 
0.2%
3.663532755 23
 
0.2%
3.950063756 21
 
0.2%
3.653756708 19
 
0.2%
3.762267391 18
 
0.2%
4.002268084 17
 
0.1%
3.86346519 17
 
0.1%
3.989898095 15
 
0.1%
3.921029622 15
 
0.1%
Other values (8483) 11231
98.3%
ValueCountFrequency (%)
2.952819531 1
 
< 0.1%
3.013070929 1
 
< 0.1%
3.065957321 1
 
< 0.1%
3.077819531 1
 
< 0.1%
3.084962501 1
 
< 0.1%
3.085055103 1
 
< 0.1%
3.146286371 1
 
< 0.1%
3.149673447 1
 
< 0.1%
3.155221529 3
< 0.1%
3.175123135 1
 
< 0.1%
ValueCountFrequency (%)
5.99695172 1
< 0.1%
5.976712038 1
< 0.1%
5.965198781 1
< 0.1%
5.949188469 1
< 0.1%
5.946815604 1
< 0.1%
5.943150979 1
< 0.1%
5.94000132 1
< 0.1%
5.927670486 1
< 0.1%
5.923982459 1
< 0.1%
5.91977335 1
< 0.1%

entropyRelative
Real number (ℝ)

HIGH CORRELATION 

Distinct7628
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8798155
Minimum0.36149865
Maximum9.5794225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:19.774177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.36149865
5-th percentile0.81548033
Q11.1747977
median1.5796825
Q32.2092793
95-th percentile3.8481982
Maximum9.5794225
Range9.2179239
Interquartile range (IQR)1.0344816

Descriptive statistics

Standard deviation1.1077368
Coefficient of variation (CV)0.58927953
Kurtosis8.4552791
Mean1.8798155
Median Absolute Deviation (MAD)0.46187025
Skewness2.4399899
Sum21486.291
Variance1.2270808
MonotonicityNot monotonic
2024-02-11T16:54:20.082416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.117812233 237
 
2.1%
3.106905073 222
 
1.9%
1.272572928 114
 
1.0%
0.7926746154 58
 
0.5%
1.265428442 56
 
0.5%
2.201145705 47
 
0.4%
2.391673469 46
 
0.4%
1.956463991 42
 
0.4%
1.959660974 41
 
0.4%
1.796744686 41
 
0.4%
Other values (7618) 10526
92.1%
ValueCountFrequency (%)
0.3614986452 1
< 0.1%
0.4023262006 1
< 0.1%
0.4217166232 1
< 0.1%
0.4330659116 2
< 0.1%
0.4391132051 1
< 0.1%
0.4530541909 1
< 0.1%
0.4609263857 1
< 0.1%
0.4714525348 2
< 0.1%
0.4768755436 1
< 0.1%
0.4801837369 1
< 0.1%
ValueCountFrequency (%)
9.579422517 1
 
< 0.1%
8.886040066 7
0.1%
8.772207853 1
 
< 0.1%
8.549317504 1
 
< 0.1%
8.516819273 10
0.1%
8.508734804 3
 
< 0.1%
8.422295877 2
 
< 0.1%
8.400488894 3
 
< 0.1%
8.3368713 2
 
< 0.1%
8.253782049 3
 
< 0.1%

vowel-cons
Real number (ℝ)

Distinct958
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41591002
Minimum0
Maximum1.1176471
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:20.381189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.33333333
median0.41666667
Q30.5
95-th percentile0.61904762
Maximum1.1176471
Range1.1176471
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.1277548
Coefficient of variation (CV)0.30716934
Kurtosis0.20838194
Mean0.41591002
Median Absolute Deviation (MAD)0.083333333
Skewness-0.050938555
Sum4753.8515
Variance0.01632129
MonotonicityNot monotonic
2024-02-11T16:54:20.670631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 509
 
4.5%
0.3333333333 451
 
3.9%
0.4 278
 
2.4%
0.4285714286 210
 
1.8%
0.25 178
 
1.6%
0.375 178
 
1.6%
0.4444444444 142
 
1.2%
0.2857142857 139
 
1.2%
0.3636363636 131
 
1.1%
0.3529411765 121
 
1.1%
Other values (948) 9093
79.6%
ValueCountFrequency (%)
0 24
0.2%
0.05555555556 1
 
< 0.1%
0.05882352941 2
 
< 0.1%
0.0625 5
 
< 0.1%
0.06666666667 14
0.1%
0.06976744186 1
 
< 0.1%
0.07142857143 9
 
0.1%
0.07692307692 13
0.1%
0.08333333333 20
0.2%
0.08695652174 2
 
< 0.1%
ValueCountFrequency (%)
1.117647059 1
< 0.1%
1 1
< 0.1%
0.9545454545 1
< 0.1%
0.9285714286 1
< 0.1%
0.9 1
< 0.1%
0.875 1
< 0.1%
0.8644067797 1
< 0.1%
0.8333333333 2
< 0.1%
0.8275862069 1
< 0.1%
0.8222222222 1
< 0.1%

firstDigitIndex
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.152143
Minimum0
Maximum240
Zeros6566
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:21.009947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q331
95-th percentile64
Maximum240
Range240
Interquartile range (IQR)31

Descriptive statistics

Standard deviation23.777508
Coefficient of variation (CV)1.4720961
Kurtosis2.912774
Mean16.152143
Median Absolute Deviation (MAD)0
Skewness1.5877076
Sum184619
Variance565.36989
MonotonicityNot monotonic
2024-02-11T16:54:21.442313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6566
57.4%
8 189
 
1.7%
33 167
 
1.5%
12 133
 
1.2%
9 131
 
1.1%
38 123
 
1.1%
35 119
 
1.0%
63 113
 
1.0%
37 111
 
1.0%
23 106
 
0.9%
Other values (116) 3672
32.1%
ValueCountFrequency (%)
0 6566
57.4%
8 189
 
1.7%
9 131
 
1.1%
10 92
 
0.8%
11 61
 
0.5%
12 133
 
1.2%
13 93
 
0.8%
14 71
 
0.6%
15 81
 
0.7%
16 80
 
0.7%
ValueCountFrequency (%)
240 1
< 0.1%
225 1
< 0.1%
163 1
< 0.1%
156 1
< 0.1%
152 1
< 0.1%
150 1
< 0.1%
146 1
< 0.1%
139 1
< 0.1%
137 1
< 0.1%
135 1
< 0.1%

length
Real number (ℝ)

HIGH CORRELATION 

Distinct323
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.120035
Minimum12
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:21.885264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile22
Q133
median47
Q371
95-th percentile131.55
Maximum1641
Range1629
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.29247
Coefficient of variation (CV)0.90465377
Kurtosis144.2501
Mean61.120035
Median Absolute Deviation (MAD)17
Skewness8.0870456
Sum698602
Variance3057.2573
MonotonicityNot monotonic
2024-02-11T16:54:22.320367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 251
 
2.2%
26 251
 
2.2%
29 250
 
2.2%
33 231
 
2.0%
27 230
 
2.0%
30 227
 
2.0%
34 224
 
2.0%
25 221
 
1.9%
35 219
 
1.9%
31 219
 
1.9%
Other values (313) 9107
79.7%
ValueCountFrequency (%)
12 1
 
< 0.1%
13 4
 
< 0.1%
14 2
 
< 0.1%
15 18
 
0.2%
16 20
 
0.2%
17 37
 
0.3%
18 56
0.5%
19 86
0.8%
20 84
0.7%
21 131
1.1%
ValueCountFrequency (%)
1641 1
< 0.1%
1386 2
< 0.1%
938 1
< 0.1%
907 1
< 0.1%
795 1
< 0.1%
648 1
< 0.1%
629 1
< 0.1%
611 1
< 0.1%
565 1
< 0.1%
557 1
< 0.1%

digits
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4521435
Minimum0
Maximum679
Zeros6566
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:22.831268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile24
Maximum679
Range679
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.319904
Coefficient of variation (CV)2.9933005
Kurtosis315.7282
Mean5.4521435
Median Absolute Deviation (MAD)0
Skewness12.097102
Sum62318
Variance266.33925
MonotonicityNot monotonic
2024-02-11T16:54:23.262942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6566
57.4%
1 660
 
5.8%
2 483
 
4.2%
4 397
 
3.5%
3 379
 
3.3%
6 376
 
3.3%
5 284
 
2.5%
8 224
 
2.0%
7 187
 
1.6%
10 160
 
1.4%
Other values (120) 1714
 
15.0%
ValueCountFrequency (%)
0 6566
57.4%
1 660
 
5.8%
2 483
 
4.2%
3 379
 
3.3%
4 397
 
3.5%
5 284
 
2.5%
6 376
 
3.3%
7 187
 
1.6%
8 224
 
2.0%
9 137
 
1.2%
ValueCountFrequency (%)
679 1
< 0.1%
269 2
< 0.1%
267 1
< 0.1%
256 1
< 0.1%
233 1
< 0.1%
222 1
< 0.1%
220 1
< 0.1%
212 1
< 0.1%
211 1
< 0.1%
201 1
< 0.1%

ip
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11328 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

Length

2024-02-11T16:54:23.696553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:23.944638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11328
99.1%
1 102
 
0.9%

special
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11430
100.0%

Length

2024-02-11T16:54:24.133298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:24.354389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 11430
100.0%

Most occurring characters

ValueCountFrequency (%)
1 11430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11430
100.0%

port
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11400 
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

Length

2024-02-11T16:54:24.554023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:24.789370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11400
99.7%
1 30
 
0.3%

subdomain
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4806649
Minimum2
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:24.970274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum25
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.369685
Coefficient of variation (CV)0.39351246
Kurtosis66.157553
Mean3.4806649
Median Absolute Deviation (MAD)0
Skewness5.7183153
Sum39784
Variance1.876037
MonotonicityNot monotonic
2024-02-11T16:54:25.200427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 6179
54.1%
4 2430
 
21.3%
2 1302
 
11.4%
5 952
 
8.3%
6 365
 
3.2%
7 80
 
0.7%
8 49
 
0.4%
9 21
 
0.2%
15 10
 
0.1%
25 8
 
0.1%
Other values (9) 34
 
0.3%
ValueCountFrequency (%)
2 1302
 
11.4%
3 6179
54.1%
4 2430
 
21.3%
5 952
 
8.3%
6 365
 
3.2%
7 80
 
0.7%
8 49
 
0.4%
9 21
 
0.2%
10 7
 
0.1%
11 5
 
< 0.1%
ValueCountFrequency (%)
25 8
0.1%
23 2
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
17 4
 
< 0.1%
16 5
< 0.1%
15 10
0.1%
14 7
0.1%
13 2
 
< 0.1%
11 5
< 0.1%

common
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7945757
Minimum2
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2024-02-11T16:54:25.425058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum11
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3584739
Coefficient of variation (CV)0.28333559
Kurtosis0.40669257
Mean4.7945757
Median Absolute Deviation (MAD)1
Skewness0.39952251
Sum54802
Variance1.8454514
MonotonicityNot monotonic
2024-02-11T16:54:25.640119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 3306
28.9%
4 3027
26.5%
6 2087
18.3%
3 1532
13.4%
7 786
 
6.9%
2 353
 
3.1%
8 246
 
2.2%
9 66
 
0.6%
10 21
 
0.2%
11 6
 
0.1%
ValueCountFrequency (%)
2 353
 
3.1%
3 1532
13.4%
4 3027
26.5%
5 3306
28.9%
6 2087
18.3%
7 786
 
6.9%
8 246
 
2.2%
9 66
 
0.6%
10 21
 
0.2%
11 6
 
0.1%
ValueCountFrequency (%)
11 6
 
0.1%
10 21
 
0.2%
9 66
 
0.6%
8 246
 
2.2%
7 786
 
6.9%
6 2087
18.3%
5 3306
28.9%
4 3027
26.5%
3 1532
13.4%
2 353
 
3.1%

hyphen
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6945 
1
4485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

Length

2024-02-11T16:54:25.878057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:26.125451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

Most occurring characters

ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6945
60.8%
1 4485
39.2%

doubleHyphen
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11401 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

Length

2024-02-11T16:54:26.318291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:26.551236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11401
99.7%
1 29
 
0.3%

shortening
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10019 
1
1411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

Length

2024-02-11T16:54:26.744507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:26.989685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10019
87.7%
1 1411
 
12.3%

abnormal
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10406 
1
 
1024

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Length

2024-02-11T16:54:27.190362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T16:54:27.420640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10406
91.0%
1 1024
 
9.0%

Interactions

2024-02-11T16:54:14.412388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:39.383001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:45.123322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:49.180491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:53.268702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:58.645009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:03.949996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:09.063761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:12.173638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:14.678532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:39.758298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:45.526485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:49.624956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:53.728186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:59.290101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:04.518097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:09.751825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:12.443561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:14.919178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:40.049512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:45.938030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:50.204622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:54.165259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:59.928834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:05.042811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:10.138017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:12.704159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:15.150315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:40.312311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:46.321148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:50.555011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:54.529493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:00.435819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:05.577488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:10.494665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:12.951605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:15.391444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:40.579105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:46.695700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:50.879107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:54.974860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:00.801770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:06.268384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:10.896286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:13.200493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:15.643503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:40.860782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:47.186897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:51.349758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:55.717813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:01.522715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:06.719856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:11.144579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:13.451726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:15.902213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:41.408904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:47.664144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:51.780010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:56.549788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:02.400092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:07.243107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:11.426700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:13.717223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:16.150737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:42.219731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:48.253646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:52.213109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:57.342619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:02.962063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:07.697210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:11.689642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:13.965411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:16.379071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:42.882834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:48.754973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:52.801797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:53:58.113957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:03.568607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:08.229536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:11.931750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-11T16:54:14.186911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-11T16:54:27.611531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
abnormalcommondigitsdoubleHyphenentropyentropyRelativefirstDigitIndexhypheniplengthngramsportshorteningstatussubdomainvowel-cons
abnormal1.000-0.105-0.0370.009-0.071-0.032-0.0300.0760.000-0.0770.0550.0090.2620.0620.0920.006
common-0.1051.0000.0330.0000.276-0.1870.0770.2310.2580.3880.1920.0000.0920.1460.2200.140
digits-0.0370.0331.0000.0000.6920.1640.9240.0620.0530.578-0.4810.0000.0120.0960.3040.082
doubleHyphen0.0090.0000.0001.0000.0670.0210.0390.0000.0000.061-0.0390.0000.0000.0340.0270.027
entropy-0.0710.2760.6920.0671.000-0.0330.6580.3980.0760.865-0.1820.0520.1320.3450.3910.313
entropyRelative-0.032-0.1870.1640.021-0.0331.0000.0690.1170.824-0.126-0.4100.0360.0540.1770.065-0.218
firstDigitIndex-0.0300.0770.9240.0390.6580.0691.0000.2660.0450.565-0.3550.0540.0550.2090.2880.119
hyphen0.0760.2310.0620.0000.3980.1170.2661.0000.0460.4730.0950.0000.0320.0650.1250.210
ip0.0000.2580.0530.0000.0760.8240.0450.0461.0000.014-0.1230.0000.0270.0900.133-0.047
length-0.0770.3880.5780.0610.865-0.1260.5650.4730.0141.0000.0590.0000.0340.1520.4470.425
ngrams0.0550.192-0.481-0.039-0.182-0.410-0.3550.095-0.1230.0591.0000.0420.0070.196-0.1520.479
port0.0090.0000.0000.0000.0520.0360.0540.0000.0000.0000.0421.0000.0000.000-0.026-0.003
shortening0.2620.0920.0120.0000.1320.0540.0550.0320.0270.0340.0070.0001.0000.1050.068-0.060
status0.0620.1460.0960.0340.3450.1770.2090.0650.0900.1520.1960.0000.1051.0000.1770.101
subdomain0.0920.2200.3040.0270.3910.0650.2880.1250.1330.447-0.152-0.0260.0680.1771.0000.066
vowel-cons0.0060.1400.0820.0270.313-0.2180.1190.210-0.0470.4250.479-0.003-0.0600.1010.0661.000

Missing values

2024-02-11T16:54:17.070279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-11T16:54:17.650849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

statusngramsentropyentropyRelativevowel-consfirstDigitIndexlengthdigitsipspecialportsubdomaincommonhyphendoubleHyphenshorteningabnormal
001021.5968113.7870431.2216030.3636360370010440000
111043.3702764.4198640.7926750.827586337717010240000
21898.0749864.7534121.1178120.5172416312619010551000
30979.6674843.4613201.9197630.3000000180010330001
401079.6803554.0976621.7786140.3636360550010351000
511019.6382393.6665301.7674320.5000000320010431000
60719.9512103.2608282.4655760.4000000190010340000
71813.0691294.7711120.7926750.405405378121010350000
801198.2227354.1955400.8312230.6363640420010340011
901119.1290724.4814561.9214690.591837201048010261000
statusngramsentropyentropyRelativevowel-consfirstDigitIndexlengthdigitsipspecialportsubdomaincommonhyphendoubleHyphenshorteningabnormal
1142001006.5160294.5268022.1477520.545455321169010361001
1142111085.8392534.6041730.8693250.476923951175010371000
1142201059.9944693.8332701.1019240.4285710250010230000
114231763.0415114.7531072.1468780.38297967854010530001
1142401053.6994074.2422781.7286120.4411760620010340000
1142501134.8067894.1190881.1864650.3571430450010350000
114261873.5511064.9385901.3513020.48888945842010660000
114270925.2996964.9875771.7967450.5208336510515010361000
114280981.4084484.2141320.5719380.3913040380010350000
114291949.2046164.8594607.1987960.53181884774111025100001

Duplicate rows

Most frequently occurring

statusngramsentropyentropyRelativevowel-consfirstDigitIndexlengthdigitsipspecialportsubdomaincommonhyphendoubleHyphenshorteningabnormal# duplicates
211485.1057223.5766184.8832130.1111111219301033000010
120923.2873084.3418653.1069050.555556384360104400008
140945.2990424.2820993.1069050.555556384250104400006
110923.2873084.2953533.1069050.555556384360104400004
70874.5202364.0841481.2292680.360000394860104500003
100923.2873084.2777983.1069050.555556384360104400003
150945.2990424.3297183.1069050.555556384250104400003
181468.0068383.3232318.8860400.12500081510102200003
191468.0068383.3232318.8860400.14285781520102200003
3311015.4093083.9746942.0221630.379310455010104300003